# Efficient and accurate tiller counting of hand-collected samples using images of straw bundles

## TL;DR

This paper introduces a new method for counting wheat tillers using images of straw bundles, achieving high accuracy comparable to manual counting.

## Contribution

The novel method combines efficient sample preparation with image analysis to achieve accurate tiller counting.

## Key findings

- The method achieves a Pearson correlation coefficient of 0.973 compared to manual counting.
- Root mean squared relative errors are below 5%.
- The TillerCounter program allows interactive adjustments for accurate tiller detection.

## Abstract

We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface (‘TillerCounter’ program) allows adjusting the automatic detections interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting.

The key contributions of our work include:1.An efficient method for imaging straw tillers based on bundle assembly.2.An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.3.Demonstration of the approach’s high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).

An efficient method for imaging straw tillers based on bundle assembly.

An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.

Demonstration of the approach’s high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).

Image, graphical abstract